Somé Nibene H, Noormohammadpour Pardis, Lange Shannon
Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada.
Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.
Front Psychiatry. 2024 Mar 4;15:1291362. doi: 10.3389/fpsyt.2024.1291362. eCollection 2024.
Machine learning is a promising tool in the area of suicide prevention due to its ability to combine the effects of multiple risk factors and complex interactions. The power of machine learning has led to an influx of studies on suicide prediction, as well as a few recent reviews. Our study distinguished between data sources and reported the most important predictors of suicide outcomes identified in the literature.
Our study aimed to identify studies that applied machine learning techniques to administrative and survey data, summarize performance metrics reported in those studies, and enumerate the important risk factors of suicidal thoughts and behaviors identified.
A systematic literature search of PubMed, Medline, Embase, PsycINFO, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Allied and Complementary Medicine Database (AMED) to identify all studies that have used machine learning to predict suicidal thoughts and behaviors using administrative and survey data was performed. The search was conducted for articles published between January 1, 2019 and May 11, 2022. In addition, all articles identified in three recently published systematic reviews (the last of which included studies up until January 1, 2019) were retained if they met our inclusion criteria. The predictive power of machine learning methods in predicting suicidal thoughts and behaviors was explored using box plots to summarize the distribution of the area under the receiver operating characteristic curve (AUC) values by machine learning method and suicide outcome (i.e., suicidal thoughts, suicide attempt, and death by suicide). Mean AUCs with 95% confidence intervals (CIs) were computed for each suicide outcome by study design, data source, total sample size, sample size of cases, and machine learning methods employed. The most important risk factors were listed.
The search strategy identified 2,200 unique records, of which 104 articles met the inclusion criteria. Machine learning algorithms achieved good prediction of suicidal thoughts and behaviors (i.e., an AUC between 0.80 and 0.89); however, their predictive power appears to differ across suicide outcomes. The boosting algorithms achieved good prediction of suicidal thoughts, death by suicide, and all suicide outcomes combined, while neural network algorithms achieved good prediction of suicide attempts. The risk factors for suicidal thoughts and behaviors differed depending on the data source and the population under study.
The predictive utility of machine learning for suicidal thoughts and behaviors largely depends on the approach used. The findings of the current review should prove helpful in preparing future machine learning models using administrative and survey data.
https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022333454 identifier CRD42022333454.
机器学习是自杀预防领域一种很有前景的工具，因为它能够综合多种风险因素的影响以及复杂的相互作用。机器学习的强大功能引发了大量关于自杀预测的研究，以及最近的一些综述。我们的研究区分了数据来源，并报告了文献中确定的自杀结果的最重要预测因素。
我们的研究旨在识别将机器学习技术应用于行政和调查数据的研究，总结这些研究中报告的性能指标，并列举所确定的自杀想法和行为的重要风险因素。
对PubMed、Medline、Embase、PsycINFO、Web of Science、护理及相关健康文献累积索引（CINAHL）和补充与替代医学数据库（AMED）进行系统文献检索，以识别所有使用机器学习通过行政和调查数据预测自杀想法和行为的研究。检索时间范围为2019年1月1日至2022年5月11日发表的文章。此外，如果符合我们的纳入标准，最近发表的三篇系统综述（其中最后一篇包括截至2019年1月1日的研究）中确定的所有文章都予以保留。使用箱线图探讨机器学习方法在预测自杀想法和行为方面的预测能力，以按机器学习方法和自杀结果（即自杀想法、自杀未遂和自杀死亡）总结受试者工作特征曲线（AUC）值的分布。按研究设计、数据来源、总样本量、病例样本量和所采用的机器学习方法，计算每种自杀结果的平均AUC及其95%置信区间（CI）。列出最重要的风险因素。
检索策略共识别出2200条独特记录，其中104篇文章符合纳入标准。机器学习算法对自杀想法和行为实现了良好的预测（即AUC在0.80至0.89之间）；然而，它们的预测能力在不同自杀结果之间似乎有所不同。提升算法对自杀想法、自杀死亡以及所有自杀结果的综合情况实现了良好的预测，而神经网络算法对自杀未遂实现了良好的预测。自杀想法和行为的风险因素因数据来源和所研究的人群而异。
机器学习对自杀想法和行为的预测效用在很大程度上取决于所使用的方法。本综述的结果应有助于使用行政和调查数据准备未来的机器学习模型。
https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022333454标识符CRD42022333454。
